Product
The readiness workspace for companies that use AI
Regolyn turns how you actually use AI into a mapped register, an honest score, and adoptable drafts. Built for the ops, legal, or engineering lead who got handed the problem.
Built for
- Operations leads who own vendor risk
- In-house counsel without an AI practice
- Engineering leads asked “are we compliant?”
Intake
A questionnaire, not a project
Regolyn asks about your AI use the way a sharp advisor would: what runs, who it touches, who checks it, what people are told. Paste your vendor list and existing policies for a sharper mapping. Nobody installs anything, and the first pass fits between two meetings.
- Plain-language questions
- Paste policies and vendor lists
- Save and resume any time
Mapping
Obligations with reasons, not vibes
Every use case is mapped against a curated, versioned obligation library covering the EU AI Act as it applies to AI users, GDPR automated decision rules, and the US state laws that reach ordinary companies. Each mapping shows its reason, grounded in your own answers, and unknowns become open questions instead of assumptions.
- Versioned library, dated entries
- Met, partial, or gap per obligation
- Open questions when facts are missing
Readiness
A score you can defend upstairs
One number, a written rationale, and the gaps that move it, ranked by severity with a concrete first step each. When the library updates, you re-run and watch the score respond to reality instead of guessing whether last quarter's work still holds.
- Score with written rationale
- Severity-ranked gaps
- Re-run as rules evolve
Drafts
The artifacts everyone keeps asking for
AI-use register entries, policy sections, and pointed vendor questions, drafted from your facts and never from boilerplate about a company that does not exist. Edit in place, copy, regenerate, and export the pack when procurement, a customer, or counsel asks.
- Register, policy, vendor questions
- Edit, copy, regenerate
- One-file export
In practice
The readiness view, on a worked example
The same interface the product ships, filled with the fictional Harbor Lane assessment.
Harbor Lane, Inc.
Worked exampleHome goods retailer, 240 people, Denver and online. Sells across the US, ships to the EU, hires remotely in New York and Chicago.
Two of three use cases carry real obligations that are only partially handled. The hiring screen is the main driver: it is high risk in four frameworks and has no audit, notice, or documented human review. The chatbot needs disclosure fixes that are quick to close. Forecasting is in good shape.
Every card below is editable in the product.
Storefront support assistant
Limited riskA vendor chatbot answers order and returns questions on the storefront, for US and EU shoppers.
Why this lane: Talks to consumers on both sides of the Atlantic but does not decide anything significant about them. Transparency rules apply rather than high-risk duties.
- EU-ART-50Transparency to peopleEuropean Union
EU shoppers chat with it, and nothing tells them they are talking to a machine at first contact.
Gap - UT-AIPADisclose generative AI useUtah, United States
It serves Utah consumers. It answers honestly if asked whether it is AI, but the disclosure is buried rather than clear and immediate.
Partial - US-FTC-5Honesty about AIUnited States, federal
Harbor Lane is responsible for what the bot promises about shipping and refunds. There is no review loop for its answers yet.
Partial - EU-ART-4AI literacy for staffEuropean Union
The support team routes escalations from the bot but has had no training on its failure modes.
Gap
Resume screening in the ATS
High riskAn ATS add-on scores and ranks inbound applicants, including for roles based in New York City and Chicago.
Why this lane: It substantially factors into hiring decisions about real candidates in NYC, Illinois, and Colorado, which puts it in the high-risk lane of the EU AI Act and three US state frameworks at once.
- NYC-LL-144Bias audit for hiring toolsNew York City, United States
Candidates for NYC roles are screened by the tool, and there is no bias audit on file and no ten-day notice to candidates.
Gap - IL-HB-3773AI in employment decisionsIllinois, United States
Chicago applicants are scored by AI with no notice that AI is used in the hiring decision.
Gap - CO-SB-205Duties for deployers of high-risk AIColorado, United States
Harbor Lane is a Colorado deployer of a high-risk system and has no impact assessment or risk management program for it.
Gap - GDPR-ART-22Solely automated decisionsEuropean Union
A recruiter glances at rankings before rejection emails go out, but nothing documents that this review is meaningful rather than rubber-stamping.
Partial - EU-ART-26Deployer duties for high-risk AIEuropean Union
If EU candidates apply, deployer duties attach: oversight is informal and the tool's logs are not being kept.
Partial
Demand forecasting
Minimal riskAn internal model predicts inventory demand by region to plan purchasing.
Why this lane: Forecasts stock, not people. No consequential decisions about individuals, so it mostly needs to be on the register and covered by basic literacy.
- EU-ART-4AI literacy for staffEuropean Union
Planners lean on the forecast daily; a short note on its known blind spots would satisfy the literacy baseline.
Partial
See where you stand this week
The free plan runs a real readiness check on two use cases: mapped obligations, a score, plain-language gaps, and drafted artifacts. Ten minutes of answers, no card.